Relation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph Representations. (arXiv:2110.02834v1 [cs.CL])
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Learning good representations on multi-relational graphs is essential to
knowledge base completion (KBC). In this paper, we propose a new
self-supervised training objective for multi-relational graph representation
learning, via simply incorporating relation prediction into the commonly used
1vsAll objective. The new training objective contains not only terms for
predicting the subject and object of a given triple, but also a term for
predicting the relation type. We analyse how this new objective impacts
multi-relational learning in KBC: experiments on a variety of datasets and
models show that relation prediction can significantly improve entity ranking,
the most widely used evaluation task for KBC, yielding a 6.1% increase in MRR
and 9.9% increase in Hits@1 on FB15k-237 as well as a 3.1% increase in MRR and
3.4% in Hits@1 on Aristo-v4. Moreover, we observe that the proposed objective
is especially effective on highly multi-relational datasets, i.e. datasets with
a large number of predicates, and generates better representations when larger
embedding sizes are used.